To successfully run a Spark job, what is required in terms of cluster nodes?

In Spark, a cluster is composed of different types of nodes, specifically the driver node and worker nodes. The driver node is responsible for orchestrating the execution of the job, coordinating tasks, and handling the overall job flow. While the driver node is essential, it is the worker nodes that actually carry out the computations on the data.

For a Spark job to run successfully, at least one worker node is necessary because this is where the tasks are executed. The worker nodes take the data and perform the computations, returning the results to the driver node. Without any worker nodes, there could be no execution of tasks, rendering the job unable to process any data.

Therefore, having at least one worker node is a fundamental requirement to ensure that the Spark job can be executed and leveraged for processing large datasets. This structure allows Spark to efficiently distribute workloads and utilize cluster resources effectively.